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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Rapid Electrochemical Diagnosis of Battery Health and Safety from Cells to Modules

Rapid electrochemical diagnosis of battery health and failure is critical for ensuring reliable battery performance and battery safety. Traditional battery health diagnostics such as capacity measurements and DC pulse tests are reliable and well-understood, however, these measurements of battery capacity and resistance do not capture all aspects of battery degradation. Other aspects of degradation, such as electrolyte decomposition, lithium-plating, and particle cracking are difficult to detect electrochemically but are crucial to measure to get a full picture of battery safety and flag out potential failures. In this work, lab- and field-aged commercial lithium-ion batteries and modules of various chemistries and formats are tested using a variety of traditional electrochemical characterization methods as well as using 2-minute pseudo-random DC pulse sequences at rest and during charge/discharge. The electrochemical measurements are compared to physical cell measurements, cell efficiency, drive cycle performance, physical and thermal heterogeneity, and qualitative safety metrics using statistical and machine-learning methods to discover if a comprehensive "battery health map" can be accurately identified using only rapid DC measurements.

ADVANCED PROPULSION SYSTEMS,ENERGY STORAGE↗
DOE OSTI2021

Using statistical modeling to predict and understand fusion experiments

Over 300 cryogenic layered direct-drive inertial confinement fusion implosions have been successfully executed on the OMEGA Laser System in the last decade. However, extracting sufficient understanding from these experiments to develop new designs or to identify or mitigate degradation sources remains challenging. Recently, a statistical modeling approach was developed to successfully design and predict improved implosion experiments on OMEGA. Here, we show that one-dimensional simulations can be used to predict the outcomes of systematically perturbed three-dimensional simulations and that this statistical modeling approach can be used to identify or rule out physical mechanisms for some of the degradation sources observed on the OMEGA Laser System for direct-drive cryogenic inertial confinement fusion. In this instance, we investigate the fusion yield dependencies on the ion temperature asymmetries and laser beam size observed in experiments by comparing with trends in three-dimensional synthetic simulation databases. Using the statistical model on these systematically perturbed simulations, we find that the statistically inferred dependency on the measured ion temperature asymmetries is well explained by imposed ℓ =1 modes. However, we find that the dependency on the laser beam size is only dominated by the illumination non-uniformity for some extreme cases.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Sulphur variations in annually layered stalagmites using benchtop micro-XRF

Variation of sulphur in annually laminated stalagmites can be used to infer the impact of past volcanic activities, anthropogenic pollution, and climate change due to increased bushfire activity. The synchrotron radiation micro-X-Ray fluorescence (SR-XRF) microprobe is a powerful tool to analyse and image sulphur recorded in stalagmites with micrometre resolution. However, access to SR-XRF beamlines can be limited, so researchers must select the most promising stalagmites for imaging. Benchtop micro-XRF is an effective tool for trace elemental analysis of speleothem samples and is a candidate for routine laboratory measurement of sulphur along stalagmite laminae and screening for SR-XRF. Here, this study describes a protocol using matrix-matched standards to measure annual variations of sulphur at trace to percent level along the laminae of two Western Australian stalagmites, one of which already having been analysed using SR-XRF. Parameters that affected quantitation include X-ray tube voltage and current, spot size of the X-ray beam and stalagmite surface roughness and porosity. The use of a 20 μm X-ray spot size provides sub-annual spatial resolution that can be completed in an overnight scan. The features in a 1000 point micro-XRF analysis of sulphur along a 20 mm transect show good consistency with SR-XRF microprobe data. Micro-XRF mapping was also performed to produce chemical images on the stalagmite and compared with Raman and X-ray diffraction (XRD) to confirm that the stalagmite is exclusively calcite, with no aragonite, and that the source of sulphur in the samples was gypsum and anhydrite. Regions of very high sulphur in the micro-XRF maps were found to be artefacts due to diffraction of the incident beam but these could be efficiently removed by using a multiple point statistics approach to produce a clean image suitable for analysis of the laminae. This work shows the potential of micro-XRF for routine analysis of sulphur in stalagmites, and to streamline sample characterisation before SR-XRF imaging.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Monitoring and prediction of porosity in laser powder bed fusion using physics-informed meltpool signatures and machine learning

In this work we accomplished the monitoring and prediction of porosity in laser powder bed fusion (LPBF) additive manufacturing process. This objective was realized by extracting physics-informed meltpool signatures from an in-situ dual-wavelength imaging pyrometer, and subsequently, analyzing these signatures via computationally tractable machine learning approaches. Porosity in LPBF occurs despite extensive optimization of processing conditions due to stochastic causes. Hence, it is essential to continually monitor the process with in-situ sensors for detecting and mitigating incipient pore formation. In this work a tall cuboid-shaped part (10 mm × 10 mm × 137 mm, material ATI 718Plus) was built with controlled porosity by varying laser power and scanning speed. This test caused various types of porosity, such as lack-of-fusion and keyhole formation, with varying degrees of severity in the part. The meltpool was continuously monitored using a dual-wavelength imaging pyrometer installed in the machine. Physically intuitive process signatures, such as meltpool length, temperature distribution, and ejecta (spatter) characteristics, were extracted from the meltpool images. Subsequently, relatively simple machine learning models, e.g., K-Nearest Neighbors, were trained to predict both the severity and type of porosity as a function of these physics-informed meltpool signatures. These models resulted in a prediction accuracy exceeding 95% (statistical F1-score). The same analysis was carried out with a complex, black-box deep learning convolutional neural network which directly used the meltpool images instead of physics-informed features. The convolutional neural network produced a comparable F1-score in the range of 89–97%. Finally, these results demonstrate that using pragmatic, physics-informed meltpool signatures within a simple machine learning model is as effective for flaw prediction in LPBF as using a complex and computationally demanding black-box deep learning model.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Implications of first neutrino-induced nuclear recoil measurements in direct detection experiments: Probing nonstandard interaction via CE ν NS

PandaX-4T and XENONnT have recently reported the first measurement of nuclear recoils induced by the B 8 solar neutrino flux, through the coherent elastic neutrino-nucleus scattering ( CE ν NS ) channel. As long anticipated, this is an important milestone for dark matter searches as well as for neutrino physics. This measurement means that these detectors have reached exposures such that searches for low mass, ≲ 10 GeV dark matter cannot be analyzed using the background-free paradigm going forward. It also opens a new era for these detectors to be used as neutrino observatories. In this paper we assess the sensitivity of these new measurements to new physics in the neutrino sector. We focus on neutrino nonstandard interactions (NSI) and show that—despite the still moderately low statistical significance of the signals—these data already provide valuable information. We find that limits on NSI from PandaX-4T and XENONnT measurements are comparable to those derived using combined COHERENT CsI and LAr data. Furthermore, they provide sensitivity to pure τ flavor parameters that are not accessible using stopped-pion or reactor sources. With larger exposures and consequently improvements of statistical uncertainties, forthcoming data from these experiments will provide important, novel results for CE ν NS -related physics. Published by the American Physical Society 2025

Sierra, D. Aristizabal↗
DOE OSTI2021

Challenging Practices of Algebraic Battery Life Models through Statistical Validation and Model Identification via Machine-Learning

Various modeling techniques are used to predict the capacity fade of Li-ion batteries. Algebraic reduced-order models, which are inherently interpretable and computationally fast, are ideal for use in battery controllers, technoeconomic models, and multi-objective optimizations. For Li-ion batteries with graphite anodes, solid-electrolyte-interphase (SEI) growth on the graphite surface dominates fade. This fade is often modeled using physically informed equations, such as square-root of time for predicting solvent-diffusion limited SEI growth, and Arrhenius and Tafel-like equations predicting the temperature and state-of-charge rate dependencies. In some cases, completely empirical relationships are proposed. However, statistical validation is rarely conducted to evaluate model optimality, and only a handful of possible models are usually investigated. This article demonstrates a novel procedure for automatically identifying reduced-order degradation models from millions of algorithmically generated equations via bi-level optimization and symbolic regression. Identified models are statistically validated using cross-validation, sensitivity analysis, and uncertainty quantification via bootstrapping. On a LiFePO 4 /Graphite cell calendar aging data set, automatically identified models utilizing square-root, power law, stretched exponential, and sigmoidal functions result in greater accuracy and lower uncertainty than models identified by human experts, and demonstrate that previously known physical relationships can be empirically "rediscovered" using machine learning.

25 ENERGY STORAGE↗
DOE OSTI2020

An Applied Strategy for Using Empirical and Hybrid Models in Online Monitoring

The monitoring of plant equipment for failure prediction is one of the key contributors to operation and maintenance (O&M) costs for a nuclear power plant (NPP) because O&M monitoring depends on labor-intensive activities that are required to meet high equipment reliability standards. These activities rely primarily on humans for information gathering, condition diagnosis, and predictive analysis. Online monitoring aims to automate these activities by relying on sensors to replace human information gathering and machine learning to replace human analysis and decision making. To facilitate automated monitoring, a systematic strategy for anomaly detection is needed to optimally use the available sensor data, empirical models, and physics-supported models. This strategy is essential to provide credible reasoning on why and when an empirical (i.e., purely data-driven) versus hybrid (i.e., physics-supported) approach should be used and to determine the ideal mix of these two approaches for a defined anomaly detection scope. The extant methods usually adopt an ad hoc trial-and-error approach that, in addition to being time-consuming and costly, is also highly subjective; it is impacted by the background and the skill set of the personnel making the decisions. Thus, such an approach cannot guarantee an optimum outcome. This represents the motivation of the current research effort, which is focused on devising a scientifically supported strategy for the optimum selection of anomaly detection methods. This report presents a detailed assessment of the main anomaly detection techniques within the empirical or hybrid method streams. Empirical methods include pattern, statistical, and causal inference. Hybrid methods include the use of physics models to train and test data methods, reduce data dimensionality, reduce data-model complexity, augment data, and reduce empirical uncertainty; hybrid methods also include the use of data to tune physics models. The listed techniques within these two streams represent the vast majority of techniques performed for anomaly detection. Using the techniques as outcomes, a strategy was developed to enable a systematic decision-making process to lead to one of these techniques. The strategy is driven by key decision points related to data relevance, simple modeling feasibility, data inference, physics-modeling value, data dimensionality, physics knowledge, method of validation, performance, data availability and suitability for training and testing, cause-effect, entropy inference, and model fitting. Each of these decision points in the strategy is explained in detail in this report with examples, along with the scientific basis behind the decisions and outcomes in common and simplified terminology. The strategy is developed for use by any NPP staff with basic engineering or science knowledge. A user-friendly graphical state flow diagram was also developed as a visual presentation of the strategy. The strategy was tested and demonstrated through two pilot projects for the application of anomaly detection at an NPP. Each pilot had two use cases: an initial case in which certain decisions were made that resulted in one or more empirical techniques and a revised use case where one or more key decisions were modified resulting in using a set of hybrid methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

A global significance evaluation method using simulated events

In High-Energy Physics experiments it is often necessary to evaluate the global statistical significance of apparent resonances observed in invariant mass spectra. One approach to determining significance is to use simulated events to find the probability of a random fluctuation in the background mimicking a real signal. As a high school summer project, we demonstrate a method with Monte Carlo simulated events to evaluate the global significance of a potential resonance with some assumptions. This method for determining significance is general and can be applied, with appropriate modification, to other resonances.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Revisiting ENSO and IOD Contributions to Australian Precipitation

Tropical modes of variability, such as El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD), exert a strong influence on the interannual variability of Australian precipitation. Nevertheless, commonly used indices of ENSO and IOD variability display significant co-variability that prevents a robust quantification of the independent contribution of each mode to precipitation anomalies. This co-variability issue is often addressed by statistically removing ENSO or IOD variability from the precipitation field before calculating teleconnection patterns. However, by performing a suite of coupled and uncoupled modeling experiments in which either ENSO or IOD variability is physically removed, we show that ENSO-only-driven precipitation patterns computed by statistically removing the IOD influence significantly underestimate the impact of ENSO on Australian precipitation variability. Here, inspired by this, we propose a conceptual model that allows one to effectively separate the contribution of each mode to Australian precipitation variability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Parametrizing the detector response with neural networks

In high energy physics, characterizing the response of a detector to radiation is one of the most important and fundamental experimental tasks. In many cases, this task is accomplished by parameterizing summary statistics of the full detector response probability density. The parameterized detector response can then be used for calibration as well as for directly improving physics analysis sensitivity. This paper reports on how to parameterize summary statistics of the detector response using neural networks. Specifically, neural networks are powerful tools for incorporating multidimensional data and the loss function used during training determines which summary statistic is learned. One common summary statistic that has not been combined with deep learning (as far as the authors are aware) is the mode. A neural network-based approach to mode learning is proposed and empirically demonstrated in the context of high energy jet calibrations. Altogether, the neural network-based toolkit for detector response parameterization can enhance the utility of data collected at high energy physics experiments and beyond.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Nonequilibrium statistical thermodynamics of multicomponent interfaces

Nonequilibrium interfacial thermodynamics has important implications for crucial biological, physical, and industrial-scale transport processes. Here, we discuss a theory of local equilibrium for multiphase multicomponent interfaces that builds upon the “sharp” interface concept first introduced by Gibbs, allowing for a description of nonequilibrium interfacial processes such as those arising in evaporation, condensation, adsorption, etc. By requiring that the thermodynamics be insensitive to the precise location of the dividing surface, one can identify conditions for local equilibrium and develop methods for measuring the values of intensive variables at the interface. We then use extensive, high-precision nonequilibrium molecular dynamics (NEMD) simulations to verify the theory and establish the validity of the local equilibrium hypothesis. In particular, we demonstrate that equilibrium equations of state are also valid out of equilibrium, and can be used to determine interfacial temperature and chemical potential(s) that are consistent with nonequilibrium generalizations of the Clapeyron and Gibbs adsorption equations. We also show, for example, that, far from equilibrium, temperature or chemical potential differences need not be uniform across an interface and may instead exhibit pronounced discontinuities. However, even in these circumstances, we demonstrate that the local equilibrium hypothesis and its implications remain valid. These results provide a thermodynamic foundation and computational tools for studying or revisiting a wide variety of interfacial transport phenomena.

36 MATERIALS SCIENCE↗
DOE OSTI2025

The CosmoVerse White Paper: Addressing observational tensions in cosmology with systematics and fundamental physics.

The standard model of cosmology has provided a good phenomenological description of a wide range of observations both at astrophysical and cosmological scales for several decades. This concordance model is constructed by a universal cosmological constant and supported by a matter sector described by the standard model of particle physics and a cold dark matter contribution, as well as very early-time inflationary physics, and underpinned by gravitation through general relativity. There have always been open questions about the soundness of the foundations of the standard model. However, recent years have shown that there may also be questions from the observational sector with the emergence of differences between certain cosmological probes. In this White Paper, we identify the key objectives that need to be addressed over the coming decade together with the core science projects that aim to meet these challenges. These discordances primarily rest on the divergence in the measurement of core cosmological parameters with varying levels of statistical confidence. These possible statistical tensions may be partially accounted for by systematics in various measurements or cosmological probes but there is also a growing indication of potential new physics beyond the standard model. After reviewing the principal probes used in the measurement of cosmological parameters, as well as potential systematics, we discuss the most promising array of potential new physics that may be observable in upcoming surveys. We also discuss the growing set of novel data analysis approaches that go beyond traditional methods to test physical models. These new methods will become increasingly important in the coming years as the volume of survey data continues to increase, and as the degeneracy between predictions of different physical models grows. There are several perspectives on the divergences between the values of cosmological parameters, such as the model-independent probes in the late Universe and model-dependent measurements in the early Universe, which we cover at length. The White Paper closes with a number of recommendations for the community to focus on for the upcoming decade of observational cosmology, statistical data analysis, and fundamental physics developments.

Dienes, Keith [Univ. of Arizona, Tucson, AZ (Unite↗
DOE OSTI2025

Breaking the curse of dimensionality: Solving configurational integrals for crystalline solids by tensor networks

Accurately evaluating configurational integrals for dense solids remains a central and difficult challenge in the statistical mechanics of condensed systems. Here, we present a tensor network approach that reformulates the high-dimensional configurational integral for identical-particle crystals into a sequence of computationally efficient summations. We represent the integrand as a high-dimensional tensor and apply tensor-train (TT) decomposition together with a custom TT-cross interpolation. This approach circumvents the need to explicitly construct the full tensor. We introduce tailored rank-1 and rank-2 schemes optimized for sharply peaked Boltzmann probability densities, typical for identical-particle crystals. When applied to the calculation of internal energy and pressure-temperature curves for crystalline Cu and Ar at high (GPa) pressures, as well as the alpha-to-beta phase transition diagram of Sn, our method accurately reproduces molecular dynamics simulation results using tight-binding, machine learning, hierarchical interacting particle–neural network, and modified embedded atom method potentials,all within seconds of computation time.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Berry Phase Effects in Dipole Density and the Mott Relation

Here, we provide a unified semiclassical theory for thermoelectric responses of any observable represented by an operator $\hat{θ}$ that is well defined in periodic crystals. The Einstein and Mott relations are established generally in the presence of Berry phase effects for various physical realizations of $\hat{θ}$ in electronic systems, including the familiar case of the electric current as well as the currently controversial cases of the spin polarization and spin current. The magnetization current, which has been proven indispensable in the thermoelectric response of electric current, is generalized to the cases of various $\hat{θ}$. In our theory the dipole density of a physical quantity emerges and plays a vital role, which contains not only the statistical sum of the dipole moment of $\hat{θ}$ but also a Berry phase correction.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Statistics of the charge spectrum of photo-multipliers and methods for absolute calibration

We derive the full expression for the shape of the charge spectrum that results from the illumination of a photo-multiplier tube. The derivation is for low intensity illumination with constant gain, a common condition for most nuclear and particle physics applications. Under these conditions, it is shown that an analytic expression can be formulated that describes details of the spectrum including the pedestal and dark noise with excellent fidelity to allow statistical fits to data. The derivation and full formula using either Gaussian or Poisson models for gain, and its limiting forms under various simplifying assumptions are presented with strategies on their use. The analytic description can be used to formulate data acquisition strategies to perform precise absolute calibration of photo-multipliers, the digitizers, and the data acquisition system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Direct cross section measurement of 102 Pd ⁢(𝛾,𝑝) and 102 Pd ⁢(𝛾,𝛼) for the astrophysical 𝑝 process

Background: A handful of neutron-deficient stable nuclei, known as the “p nuclei,” cannot be produced through astrophysical neutron capture processes. Instead, some of these nuclei are proposed to be produced by 𝛾-induced reactions on existing r- and s-process seeds. The specific astrophysical site or sites are not yet identified, however, with uncertainties in the cross sections of these 𝛾-induced reactions playing a role. Databases of reaction rates for astrophysical simulations often rely on theoretical statistical model calculations, such as Hauser-Feshbach, for rates where no experimental information is known. However, reasonable variations in the choice of parametrizations of various nuclear properties can create order-of-magnitude variations in the final predicted cross sections and reaction rates, which are then propagated through the models to the predicted final abundances. Purpose: To better constrain these statistical model calculations and ultimately reduce the uncertainties from the nuclear physics on our understanding of the p nuclei, a measurement of the cross sections of 𝛾-induced reactions on the p-nucleus 102 Pd was undertaken. This work represents the first measurement of its kind, using segmented silicon detectors to measure prompt charged particle emission from 𝛾-induced reactions. Methods: Quasimonoenergetic gamma beams from the High Intensity 𝛾 Source facility bombarded an enriched 102 Pd target. A segmented silicon array was arranged to detect the particles emitted from (𝛾,𝑝) and (𝛾,𝛼) reactions. Results: Reaction cross sections were deduced at multiple 𝛾-beam energies between 10 and 19 MeV, and compared to statistical model calculations using talys-1.96. The 102 Pd ⁢(𝛾,𝑝)⁢ 101 Rh reaction cross section was reasonably well reproduced by a subset of photon strength functions and level densities, though the strength to the ground state of 101 Rh was underestimated at higher incident gamma energies. The 102 Pd ⁢(𝛾,𝛼)⁢ 98 Ru was in general overpredicted by the various alpha-nucleus optical model potentials. Conclusions: While the theoretical cross sections used to model the (𝛾,𝑝) reactions for the p process may be reasonable, a more careful approach is needed in the case of (𝛾,𝛼). Further work to probe gamma-induced reaction cross sections at and near the p nuclei is warranted.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗